Integrating GCN, BiLSTM, and Attention for Accurate Short-Term Traffic Forecasting on Urban Road Networks

DOI

10.2991/978-94-6463-986-5_55How to use a DOI?

Keywords

Graph Convolutional Network; Bidirectional Long Short-Term Memory; Attention Mechanism; Spatio-Temporal Modeling

Abstract

Urban traffic congestion has become a global challenge, causing economic losses, environmental pollution, and reduced quality of life. Accurate short-term traffic flow prediction is essential for optimizing traffic management, improving travel efficiency, and supporting the development of intelligent transportation systems. When applied to large-scale traffic systems, conventional statistical models frequently fail to account for the highly nonlinear and interdependent spatio-temporal patterns. This paper presents a GCN-BiLSTM-Attention model for short-term traffic flow prediction. This model integrates graph convolution to capture spatial dependencies, bidirectional LSTM for temporal modeling, and an attention mechanism to enhance interpretability. Evaluated on the METR-LA dataset with 207 traffic sensors, the model achieves an MAE of 1.96 mph, RMSE of 4.25 mph, and ±10% accuracy of 91.58%. A comprehensive data preprocessing pipeline—including anomaly removal, imputation, and normalization—ensures high-quality input. Attention weight analysis shows a focus on recent time steps, aligning with traffic dynamics. Results demonstrate the model’s effectiveness in learning complex spatio-temporal patterns and its potential for deployment in intelligent transportation systems.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xuanhao Tian
PY  - 2026
DA  - 2026/02/18
TI  - Integrating GCN, BiLSTM, and Attention for Accurate Short-Term Traffic Forecasting on Urban Road Networks
BT  - Proceedings of the 2025 International Conference on Electronics, Electrical and Grid Technology (ICEEGT 2025)
PB  - Atlantis Press
SP  - 535
EP  - 545
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-986-5_55
DO  - 10.2991/978-94-6463-986-5_55
ID  - Tian2026
ER  -